Stress impacts our physical and mental health as well as our social life. A passive and contactless indoor stress monitoring system can unlock numerous important applications such as workplace productivity assessment, smart homes, and personalized mental health monitoring. While the thermal signatures from a user's body captured by a thermal camera can provide important information about the "fight-flight" response of the sympathetic and parasympathetic nervous system, relying solely on thermal imaging for training a stress prediction model often lead to overfitting and consequently a suboptimal performance. This paper addresses this challenge by introducing ThermaStrain, a novel co-teaching framework that achieves high-stress prediction performance by transferring knowledge from the wearable modality to the contactless thermal modality. During training, ThermaStrain incorporates a wearable electrodermal activity (EDA) sensor to generate stress-indicative representations from thermal videos, emulating stress-indicative representations from a wearable EDA sensor. During testing, only thermal sensing is used, and stress-indicative patterns from thermal data and emulated EDA representations are extracted to improve stress assessment. The study collected a comprehensive dataset with thermal video and EDA data under various stress conditions and distances. ThermaStrain achieves an F1 score of 0.8293 in binary stress classification, outperforming the thermal-only baseline approach by over 9%. Extensive evaluations highlight ThermaStrain's effectiveness in recognizing stress-indicative attributes, its adaptability across distances and stress scenarios, real-time executability on edge platforms, its applicability to multi-individual sensing, ability to function on limited visibility and unfamiliar conditions, and the advantages of its co-teaching approach.

ThermaStrain是一种创新的共学习框架，通过将可穿戴的电皮肤活动（EDA）传感器与非接触的热量模态进行知识转移，从而在压力预测中实现高性能。该研究收集了包含各种压力条件和距离的热量视频和EDA数据的全面数据集。ThermaStrain在二进制压力分类方面达到了0.8293的F1得分，优于仅使用热量的基准方法超过9%。广泛的评估突出了ThermaStrain在识别压力相关属性、距离和压力场景的适应性、边缘平台上的实时可执行性、多个个体感知的适用性、在有限可见性和陌生条件下发挥作用的能力以及其共学习方法的优势。

热读之间：“ 共同教学身体热特征，非侵入式应激检测